Study of General Robust Subband Adaptive Filtering

TL;DR

This paper introduces a versatile robust subband adaptive filtering scheme that effectively handles impulsive noise, offering fast convergence and low steady-state error, with demonstrated superior performance in system identification and echo cancellation tasks.

Contribution

It proposes a general robust subband adaptive filtering framework that unifies various robust criteria and achieves improved convergence and accuracy.

Findings

Outperforms existing methods in impulsive noise environments.

Achieves fast convergence and low steady-state error.

Effective in system identification and echo cancellation scenarios.

Abstract

In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Specifically, by choosing different scaling factors such as from the M-estimate and maximum correntropy robust criteria in the GR-SAF scheme, we can easily obtain different GR-SAF algorithms. Importantly, the proposed GR-SAF algorithm can be reduced to a variable regularization robust normalized SAF algorithm, thus having fast convergence rate and low steady-state error. Simulations in the contexts of system identification with impulsive noise and echo cancellation with double-talk have verified that the proposed GR-SAF algorithms outperforms its counterparts.